Adaptive Control and Market Integration: Optimizing Distributed Power Resources for a Sustainable Grid by Josue N. Otshwe, Bin Li, Songsong Chen, Feixiang Gong, Bing Qi, Ngouokoua J. Chabrol

“…System performance is improved using advanced control strategies together with real-time market-responsive changes and predictive algorithms. The efficacy of the proposed methodology is validated through a detailed simulation of a small island grid using mixed-integer linear programming (MILP) and particle swarm optimization (PSO), which demonstrates significant operational improvements. …”
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Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study by Hussam J. Khasawneh, Waseem M. Al-Khatib, Zaid A. Ghazal, Ahmad M. Al-Hadi, Zaid M. Arabiyat, Osama Habahbeh

“…Our approach overcomes these limitations by employing ML algorithms to accurately predict solar generation patterns, enabling more efficient scheduling of electrical appliances. …”
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Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector by Germán Rodríguez-Galán, Eduardo Benavides-Astudillo, Daniel Nuñez-Agurto, Pablo Puente-Ponce, Sonia Cárdenas-Delgado, Mauricio Loachamín-Valencia

“…Machine learning models were developed to classify suspicious web domains and predict their vulnerability to XSS attacks. Additionally, clustering algorithms enabled user segmentation based on cookie data, identification of behavioral patterns, enhanced personalized web recommendations, and browsing experience optimization. …”
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Simulation of Quantum Dynamics Based on the Quantum Stochastic Differential Equation by Ming Li

“…The numerical simulation algorithm of stochastic process of direct photodetection of a driven two-level system for the predictions of the dynamical behavior is proposed. …”
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Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering by Yanhong Chen, Haibin Cai, Yiqing Gong, Kun Lu, Jingqiao Mao, Weiyu Chen, Kang Wang, Huan Gao, Mingming Tian

“…This study demonstrates the potential of employing unsupervised clustering algorithms for identifying the diurnal dynamics of the phytoplankton distribution under complex influences, which can aid in optimizing the management and monitoring of shallow lakes.…”
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A Comprehensive Overview of the Development and Research of Energy Savings of Electric Wheel Loader by Xiaotao Fei, Zuo Cheng, Shaw Voon Wong, Muhammad Amin Azman, Dawei Wang, Xiuxian Zhang, Qiuchen Shao, Qingqiu Lin

“…The analysis reveals that integrating optimal control strategies with machine learning algorithms—such as model predictive control (MPC)—is a critical pathway to achieving energy-efficient and assisted driving in next-generation EWLs. …”
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Artificial intelligence-based automated breast ultrasound radiomics for breast tumor diagnosis and treatment: a narrative review by Yinglin Guo, Ning Li, Chonghui Song, Juan Yang, Yinglan Quan, Hongjiang Zhang

“…Radiomics, an emerging field, focuses on extracting high-dimensional quantitative features from medical imaging data and utilizing them to construct predictive models for disease diagnosis, prognosis, and treatment evaluation. …”
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Combination of Remote Sensing and Artificial Intelligence in Fruit Growing: Progress, Challenges, and Potential Applications by Danielle Elis Garcia Furuya, Édson Luis Bolfe, Taya Cristo Parreiras, Jayme Garcia Arnal Barbedo, Thiago Teixeira Santos, Luciano Gebler

“…The analysis included the use of remote sensing (orbital and proximal) imagery and ML/DL algorithms to map crop areas, detect diseases, and monitor crop development, among other analyses. …”
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Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization by Badril Azhar, Muhammad Ikhsan Taipabu, Cries Avian, Karthickeyan Viswanathan, Wei Wu, Raymond Lau

“…A ML model evaluation, using various algorithms, identify XGBoost, Extra Trees, Gradient Boosting, LGBM, and Random Forest demonstrate the best performer for predicting process parameters, achieving an R2 value of nearly to 1. …”
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Determining of the laser heat flux for three-dimensional conduction model by the sequential method by Nguyen Nhat Phi Long, Son Hoai Nguyen, Nguyen Quan

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Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease by Daryll Philip, Rebecca Hodgkiss, Swarnima Kollampallath Radhakrishnan, Akshat Sinha, Animesh Acharjee

“…These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. …”
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Land cover change mitigated 20 % of the total increased evapotranspiration in tropical Lancang-Mekong River Basin during 2001–2019 by Houbing Chen, Yaoliang Chen, Lamin R. Mansaray, Longwei Li, Shusen Wang

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Enhanced Detection of Intrusion Detection System in Cloud Networks Using Time-Aware and Deep Learning Techniques by Nima Terawi, Huthaifa I. Ashqar, Omar Darwish, Anas Alsobeh, Plamen Zahariev, Yahya Tashtoush

“…We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). …”
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The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach by Shiqi Zhang, Shiqi Zhang, Jianan Jin, Benfeng Xu, Qi Zheng, Haibo Mou

“…Nine machine learning algorithms were used to build models: AdaBoost, GBM, KNN, lightGBM, MLP, RF, SVM, XGBoost, and logistics. …”
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Engineering Synthetic Microbial Communities: Diversity and Applications in Soil for Plant Resilience by Arneeb Tariq, Shengzhi Guo, Fozia Farhat, Xihui Shen

“…Artificial intelligence-driven models can predict complex microbial interactions, while machine learning algorithms can analyze vast datasets to identify key microbial taxa and their functions. …”
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Graph Split Federated Learning for Distributed Large-Scale AIoT in Smart Cities by Hanyue Xu, Kah Phooi Seng, Li-Minn Ang, Wei Wang, Jeremy Smith

“…The framework’s efficacy is validated through predictive modeling of autonomous vehicle passenger demand using real-world IoT data. …”
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Applications of fault-tolerant control system in the design of wind turbine generation systems: A comprehensive review and future prospects by Arslan Ahmed Amin, Muhammad Irfan, Turki Alsuwian, Saifur Rahman, Ansa Mubarak, Saba Waseem

“…The review also discusses the relevant research gaps and future works, such as using computational algorithms to develop novel material and implementing the utilization of digital twins to improve fault management.…”
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Designing an Intelligent Scoring System for Crediting Manufacturers and Importers of Goods in Industry 4.0 by Mohsin Ali, Abdul Razaque, Joon Yoo, Uskenbayeva Raissa Kabievna, Aiman Moldagulova, Satybaldiyeva Ryskhan, Kalpeyeva Zhuldyz, Aizhan Kassymova

“…On the other hand, the capabilities of AI-driven credit assessment algorithms enable more precise, effective, and customized credit choices that are specifically tailored to meet the unique financial profiles of manufacturers and importers. …”
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Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis by Renhai Feng, Khan Wajid, Muhammad Faheem, Jiang Wang, Fazal E. Subhan, Muhammad Shoaib Bhutta

“…Moreover, its emphasis the importance of computed and estimated indices obtained through UPINN for predicting voltage collapse occurrences within the system.…”
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Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach by Yehoon Jo, Mi-Yeon Shin, Sungkyoon Kim

“…The ML models provided robust predictive performance and novel insights into chemical and metabolic interactions, advocating for regulatory measures to reduce harmful exposures and integrate environmental factors into MetS prevention strategies.…”
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